U.S. patent application number 16/247746 was filed with the patent office on 2020-07-16 for method of diagnosing a propulsion system of a vehicle, and a system therefor.
This patent application is currently assigned to GM Global Technology Operations LLC. The applicant listed for this patent is GM Global Technology Operations LLC. Invention is credited to Chen-fang Chang, Shiming Duan, Ibrahim Haskara, Chunhao J. Lee, Azeem Sarwar.
Application Number | 20200224601 16/247746 |
Document ID | / |
Family ID | 71131844 |
Filed Date | 2020-07-16 |
United States Patent
Application |
20200224601 |
Kind Code |
A1 |
Haskara; Ibrahim ; et
al. |
July 16, 2020 |
METHOD OF DIAGNOSING A PROPULSION SYSTEM OF A VEHICLE, AND A SYSTEM
THEREFOR
Abstract
A method of diagnosing a propulsion system implements a top-down
hierarchical examination procedure, in which the propulsion system
is analyzed as a whole to determine if the propulsion system is
healthy. Data from a first set of vehicle sensors is compared to a
system-healthy data cluster to determine if the propulsion system
is healthy or unhealthy. If the propulsion system is unhealthy,
then a plurality of subsystems of the propulsion system are each
analyzed at a first examination level using selective data from the
sensors to identify one of the subsystems as an unhealthy
subsystem. A plurality of component systems of the unhealthy
subsystem are then analyzed at a second examination level using
other selective data from the sensors to identify one of the
component systems of the unhealthy subsystem as an unhealthy
component system.
Inventors: |
Haskara; Ibrahim; (Macomb,
MI) ; Chang; Chen-fang; (Bloomfield Hills, MI)
; Duan; Shiming; (Ann Arbor, MI) ; Lee; Chunhao
J.; (Troy, MI) ; Sarwar; Azeem; (Rochester
Hills, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GM Global Technology Operations LLC |
Detroit |
MI |
US |
|
|
Assignee: |
GM Global Technology Operations
LLC
Detroit
MI
|
Family ID: |
71131844 |
Appl. No.: |
16/247746 |
Filed: |
January 15, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G07C 5/0808 20130101;
F02D 41/22 20130101; F02D 41/26 20130101; F02D 11/107 20130101 |
International
Class: |
F02D 41/22 20060101
F02D041/22; G07C 5/08 20060101 G07C005/08; F02D 11/10 20060101
F02D011/10 |
Claims
1. A method of diagnosing a propulsion system of a vehicle, the
method comprising: defining a first set of a plurality of sensors
of the vehicle for evaluating an overall status of the propulsion
system; defining a system-healthy data cluster, wherein the
system-healthy data cluster defines an inclusive range of data
values from the first set of the plurality of sensors indicating a
healthy status of the propulsion system, and wherein the
system-healthy data cluster is saved on a memory of a computing
device of the vehicle; sensing data from the first set of the
plurality of sensors; comparing the data sensed from the first set
of the plurality of sensors to the system-healthy data cluster,
with the computing device, to determine if the data sensed from the
first set of the plurality of sensors is within the system-healthy
data cluster, or if the data sensed from the first set of the
plurality of sensors is at least partially outside the
system-healthy data cluster; indicating that the propulsion system
is unhealthy, with the computing device, when the data sensed from
the first set of the plurality of sensors is at least partially
outside the system-healthy data cluster; and analyzing the
propulsion system using a top-down hierarchical examination
procedure, with the computing device, when the propulsion system is
unhealthy, in which a plurality of subsystems of the propulsion
system are analyzed at a first examination level using selective
data from the plurality of sensors to identify one of the plurality
of subsystems as an unhealthy subsystem, and then a plurality of
component systems of the unhealthy subsystem are analyzed at a
second examination level using other selective data from the
plurality of sensors to identify one of the plurality of component
systems as an unhealthy component system.
2. The method set forth in claim 1, wherein the plurality of
subsystems of the propulsion system includes a first subsystem, and
wherein analyzing the propulsion system using the top-down
hierarchical examination procedure includes determining if the
first subsystem of the propulsion system is the unhealthy
subsystem, with the computing device, based on the data sensed from
the first set of the plurality of sensors.
3. The method set forth in claim 2, further comprising defining a
first subsystem-status data cluster for the first subsystem,
wherein the first subsystem-status data cluster defines a range of
data values from the first set of the plurality of sensors
indicating that the first subsystem is the unhealthy subsystem, and
wherein the first subsystem-status data cluster is saved on the
memory of the computing device.
4. The method set forth in claim 3, wherein determining if the
first subsystem is the unhealthy subsystem includes comparing the
data sensed from the first set of the plurality of sensors to the
first subsystem-status data cluster, with the computing device, to
determine if the data sensed from the first set of the plurality of
sensors is within the first subsystem-status data cluster, or if
the data sensed from the first set of the plurality of sensors is
outside the first subsystem-status data cluster.
5. The method set forth in claim 4, further comprising indicating
that the first subsystem is the unhealthy subsystem, with the
computing device, when the data sensed from the first set of the
plurality of sensors is inside of the first subsystem-status data
cluster.
6. The method set forth in claim 5, wherein the plurality of
component systems of the first subsystem includes a first component
system, and wherein the method further comprises defining a first
component-status data cluster for the first component system of the
first subsystem, wherein the first component-status data cluster
defines a range of data values from a second set of the plurality
of sensors indicating that the first component system of the first
subsystem is the unhealthy component, and wherein the first
component-status data cluster is saved on the memory of the
computing device.
7. The method set forth in claim 6, further comprising comparing
the data sensed from the second set of the plurality of sensors to
the first component-status data cluster, with the computing device,
to determine if the data sensed from the second set of the
plurality of sensors is within the first component-status data
cluster, or if the data sensed from the second set of the plurality
of sensors is outside the first component-status data cluster, when
the first system is un-healthy.
8. The method set forth in claim 7, further comprising indicating
that the first component system of the first subsystem is the
unhealthy component system, with the computing device, when the
data sensed from the second set of the plurality of sensors is
inside of the first component-status data cluster.
9. The method set forth in claim 1, characterized by not performing
additional diagnostic tests on the plurality of subsystems and on
the plurality of component systems of each of the plurality of
subsystems, when the data sensed from the first set of the
plurality of sensors is inside the system-healthy data cluster.
10. The method set forth in claim 1, further comprising
manipulating the data sensed from the first set of the plurality of
sensors to define a data value, and using the data value to compare
to the system-healthy data cluster to determine if the data sensed
from the first set of the plurality of sensors is within the
system-healthy data cluster, or if the data sensed from the first
set of the plurality of sensors is outside the system-healthy data
cluster.
11. The method set forth in claim 1, further comprising
communicating data from the computing device of the vehicle to a
computer located remotely from the vehicle, wherein the computer
located remotely from the vehicle implements at least a portion of
the top-down hierarchical examination procedure.
12. A diagnostic system for diagnosing a propulsion system of a
vehicle, the diagnostic system comprising: a plurality of sensors
operable to sense data related to operation of the propulsion
system; a computing device in communication with the plurality of
sensors, the computing device including a processor and a memory
having a system-healthy data cluster, and a diagnostic algorithm
stored thereon, wherein the processor is operable to execute the
diagnostic algorithm to: sense data from a first set of the
plurality of sensors; compare the data sensed from the first set of
the plurality of sensors to the system-healthy data cluster to
determine if the data sensed from the first set of the plurality of
sensors is within the system-healthy data cluster, or if the data
sensed from the first set of the plurality of sensors is at least
partially outside the system-healthy data cluster, wherein the
system-healthy data cluster defines an inclusive range of data
values from the first set of the plurality of sensors indicating a
healthy status of the propulsion system; indicate that the
propulsion system is unhealthy when the data sensed from the first
set of the plurality of sensors is at least partially outside the
system-healthy data cluster; and analyze the propulsion system
using a top-down hierarchical examination procedure when the
propulsion system is unhealthy, in which a plurality of subsystems
of the propulsion system are analyzed at a first examination level
using selective data from the plurality of sensors to identify one
of the plurality of subsystems as an unhealthy subsystem, and then
a plurality of component systems of the unhealthy subsystem are
analyzed at a second examination level using other selective data
from the plurality of sensors to identify one of the plurality of
component systems as an unhealthy component system.
13. The diagnostic system set forth in claim 12, further comprising
a first subsystem-status data cluster saved on the memory of the
computing device, wherein the first subsystem-status data cluster
defines a range of data values from the first set of the plurality
of sensors indicating that a first subsystem of the propulsion
system is the unhealthy subsystem.
14. The diagnostic system set forth in claim 13, wherein the
processor is operable to execute the diagnostic algorithm to
compare data sensed from the first set of the plurality of sensors
to the first subsystem-status data cluster to determine if the data
sensed from the first set of the plurality of sensors is within the
first subsystem-status data cluster, or if the data sensed from the
first set of the plurality of sensors is outside the first
subsystem-status data cluster.
15. The diagnostic system set forth in claim 14, wherein the
processor is operable to execute the diagnostic algorithm to
indicate that the first subsystem is the unhealthy subsystem when
the data sensed from the first set of the plurality of sensors is
inside of the first subsystem-status data cluster.
16. The diagnostic system set forth in claim 15, further comprising
a first component-status data cluster saved on the memory of the
computing device, wherein the first component-status data cluster
defines a range of data values from a second set of the plurality
of sensors indicating that a first component system of the first
subsystem is the unhealthy component system.
17. The diagnostic system set forth in claim 16, wherein the
processor is operable to execute the diagnostic algorithm to
compare data sensed from the second set of the plurality of sensors
to the first component-status data cluster, when the first
subsystem is the unhealthy subsystem, to determine if the data
sensed from the second set of the plurality of sensors is within
the first component-status data cluster, or if the data sensed from
the second set of the plurality of sensors is outside the first
component-status data cluster.
18. The diagnostic system set forth in claim 17, wherein the
processor is operable to indicate that the first component system
of the first subsystem is the unhealthy component system when the
data sensed from the second set of the plurality of sensors is
inside of the first component-status data cluster.
19. A vehicle comprising: a propulsion system having a plurality of
subsystems, with each of the plurality of subsystems having a
plurality of components; a plurality of sensors operable to sense
data related to operation of the propulsion system; a diagnostic
system disposed in communication with the plurality of sensors and
operable to receive data from the plurality of sensors, wherein the
diagnostic system includes a processor and a memory having a
system-healthy data cluster, and a diagnostic algorithm stored
thereon, wherein the processor is operable to execute the
diagnostic algorithm to: sense data from a first set of the
plurality of sensors; compare the data sensed from the first set of
the plurality of sensors to the system-healthy data cluster to
determine if the data sensed from the first set of the plurality of
sensors is within the system-healthy data cluster, or if the data
sensed from the first set of the plurality of sensors is at least
partially outside the system-healthy data cluster, wherein the
system-healthy data cluster defines an inclusive range of data
values from the first set of the plurality of sensors indicating a
healthy status of the propulsion system; indicate that the
propulsion system is unhealthy when the data sensed from the first
set of the plurality of sensors is at least partially outside the
system-healthy data cluster; and analyze the propulsion system
using a top-down hierarchical examination procedure when the
propulsion system is unhealthy, in which a plurality of subsystems
of the propulsion system are analyzed at a first examination level
using selective data from the plurality of sensors to identify one
of the plurality of subsystems as an unhealthy subsystem, and then
a plurality of component systems of the unhealthy subsystem are
analyzed at a second examination level using other selective data
from the plurality of sensors to identify one of the plurality of
component systems as an unhealthy component system.
20. The vehicle set forth in claim 19, further comprising: a first
subsystem-status data cluster saved on the memory of the computing
device, wherein the first subsystem-status data cluster defines a
range of data values from the first set of the plurality of sensors
indicating that a first subsystem of the propulsion system is the
unhealthy subsystem; wherein the processor is operable to execute
the diagnostic algorithm to compare data sensed from the first set
of the plurality of sensors to the first subsystem-status data
cluster to determine if the data sensed from the first set of the
plurality of sensors is within the first subsystem-status data
cluster, or if the data sensed from the first set of the plurality
of sensors is outside the first subsystem-status data cluster;
wherein the processor is operable to execute the diagnostic
algorithm to indicate that the first subsystem is the unhealthy
subsystem when the data sensed from the first set of the plurality
of sensors is inside of the first subsystem-status data cluster; a
first component-status data cluster saved on the memory of the
computing device, wherein the first component-status data cluster
defines a range of data values from a second set of the plurality
of sensors indicating that a first component system of the first
subsystem is the unhealthy component system; wherein the processor
is operable to execute the diagnostic algorithm to compare data
sensed from the second set of the plurality of sensors to the first
component-status data cluster, when the first subsystem is the
unhealthy subsystem, to determine if the data sensed from the
second set of the plurality of sensors is within the first
component-status data cluster, or if the data sensed from the
second set of the plurality of sensors is outside the first
component-status data cluster; and wherein the processor is
operable to execute the diagnostic algorithm to indicate that the
first component system of the first subsystem is the unhealthy
component system when the data sensed from the second set of the
plurality of sensors is inside of the first component-status data
cluster.
Description
INTRODUCTION
[0001] The disclosure generally relates to a method of diagnosing a
propulsion system of a vehicle, and a diagnostic system
therefor.
[0002] A propulsion system for a vehicle includes many different
subsystems, with each subsystem having several different
components. Each individual component of one of the subsystems may
additionally have several sub-components. The vehicle includes many
different sensors for sensing data related to the operation of the
propulsion system. The vehicle may run an individual diagnostic
test for many of the different components/sub-components of the
different subsystems in order to determine if the
components/sub-components are operating properly, i.e., healthy, or
if they are not operating properly, i.e., unhealthy. This
constitutes a bottom-up strategy, in which each
component/subcomponent of the propulsion system is analyzed with a
respective diagnostic test to determine the health of that
respective component/subcomponent.
SUMMARY
[0003] A method of diagnosing a propulsion system of a vehicle is
provided. The method includes defining a first set of a plurality
of sensors of the vehicle for evaluating an overall status of the
propulsion system. A system-healthy data cluster is defined, and
saved on a memory of a computing device of the vehicle. The
system-healthy data cluster defines an inclusive range of data
values from the first set of the plurality of sensors indicating a
healthy status of the propulsion system. Data from the first set of
the plurality of sensors is sensed. The computing device compares
the data sensed from the first set of the plurality of sensors to
the system-healthy data cluster to determine if the data sensed
from the first set of the plurality of sensors is within the
system-healthy data cluster, or if the data sensed from the first
set of the plurality of sensors is at least partially outside the
system-healthy data cluster. When the data sensed from the first
set of the plurality of sensors is at least partially outside the
system-healthy data cluster, then the computing device indicates
that the propulsion system is unhealthy. When the propulsion system
is unhealthy, the computing system analyzes the propulsion system
using a top-down hierarchical examination procedure, in which a
plurality of subsystems of the propulsion system are analyzed at a
first examination level using selective data from the plurality of
sensors to identify one of the plurality of subsystems as an
unhealthy subsystem, and then a plurality of component systems of
the unhealthy subsystem are analyzed at a second examination level
using other selective data from the plurality of sensors to
identify one of the plurality of component systems as an unhealthy
component system.
[0004] In one aspect of the method of diagnosing the propulsion
system, the plurality of subsystems of the propulsion system
includes a first subsystem. The computing device analyzes the
propulsion system using the top-down hierarchical examination
procedure by determining if the first subsystem of the propulsion
system is the unhealthy subsystem, based on the data sensed from
the first set of the plurality of sensors. The computing device may
further determine if a second subsystem, a third subsystem, etc.,
of the propulsion system are unhealthy subsystems, based on the
data sensed from the first set of the plurality of sensors.
[0005] In one aspect of the method of diagnosing the propulsion
system, a first subsystem-status data cluster for the first
subsystem is defined, and saved in the memory of the computing
device. The first subsystem-status data cluster defines a range of
data values from the first set of the plurality of sensors
indicating that the first subsystem is the unhealthy subsystem. The
computing device may then compare the data sensed from the first
set of the plurality of sensors to the first subsystem-status data
cluster, to determine if the data sensed from the first set of the
plurality of sensors is within the first subsystem-status data
cluster, or if the data sensed from the first set of the plurality
of sensors is outside the first subsystem-status data cluster. When
the data sensed from the first set of the plurality of sensors is
inside of the first subsystem-status data cluster, the computing
device may then indicate that the first subsystem is the unhealthy
subsystem.
[0006] In one aspect of the method of diagnosing the propulsion
system, the plurality of components of the first subsystem includes
a first component system. A first component-status data cluster for
the first component system of the first subsystem is defined, and
saved in the memory of the computing device. The first
component-status data cluster defines a range of data values from a
second set of the plurality of sensors indicating that the first
component system of the first subsystem is the unhealthy component
system. When the first subsystem is the unhealthy subsystem, the
computing device compares data sensed from the second set of the
plurality of sensors to the first component-status data cluster to
determine if the data sensed from the second set of the plurality
of sensors is within the first component-status data cluster, or if
the data sensed from the second set of the plurality of sensors is
outside the first component-status data cluster. When the data
sensed from the second set of the plurality of sensors is inside of
the first component-status data cluster, then the computing device
indicates that the first component system of the first subsystem is
the unhealthy component system.
[0007] In one aspect of the method of diagnosing the propulsion
system, the method is characterized by the top-down hierarchical
examination procedure, which examines the subsystems and components
of the subsystem in a top-down order to identify the root cause of
the unhealthy system. By so doing, the process described herein
does not perform additional diagnostic tests on the plurality of
subsystems and on the plurality of components of each of the
plurality of subsystems, when the data sensed from the first set of
the plurality of sensors is inside the system-healthy data cluster.
In other words, if the propulsion system is healthy, i.e., the data
sensed from the first set of the plurality of sensors is within the
system-healthy data cluster, then the process does not perform
additional diagnostic tests, thereby reducing the computational
demands on the computing device and improving the efficiency of the
diagnostic system.
[0008] In one aspect of the method of diagnosing the propulsion
system, the computing device may manipulate the data sensed from
the first set of the plurality of sensors to define a data value.
The computing device may then use the data value to compare to the
system-healthy data cluster to determine if the data sensed from
the first set of the plurality of sensors is within the
system-healthy data cluster, or if the data sensed from the first
set of the plurality of sensors is outside the system-healthy data
cluster. The data value may be calculated or defined for a period
of time to define a running average, or to define multiple
independent data values.
[0009] A vehicle is also provided. The vehicle includes a
propulsion system having a plurality of subsystems. Each of the
plurality of subsystems may include a plurality of components. The
vehicle includes a plurality of sensors that are operable to sense
data related to operation of the propulsion system. A diagnostic
system is disposed in communication with the plurality of sensors,
and is operable to receive data from the plurality of sensors. The
diagnostic system includes a processor, and a memory having a
system-healthy data cluster, and a diagnostic algorithm stored
thereon. The processor is operable to execute the diagnostic
algorithm to implement a method of diagnosing the propulsion
system. More particularly, the processor executes the diagnostic
algorithm to sense data from a first set of the plurality of
sensors. The data sensed from the first set of the plurality of
sensors is compared to the system-healthy data cluster to determine
if the data sensed from the first set of the plurality of sensors
is within the system-healthy data cluster, or if the data sensed
from the first set of the plurality of sensors is outside the
system-healthy data cluster. The system-healthy data cluster
defines an inclusive range of data values from the first set of the
plurality of sensors indicating a healthy status of the propulsion
system. When the data sensed from the first set of the plurality of
sensors is outside the system-healthy data cluster, the diagnostic
algorithm indicates that the propulsion system is unhealthy, and
proceeds to analyze the propulsion system using a top-down
hierarchical examination procedure. The top-down hierarchical
examination procedure analyzes a plurality of subsystems of the
propulsion system at a first examination level using selective data
from the plurality of sensors to identify one of the plurality of
subsystems as an unhealthy subsystem, and then a plurality of
component systems of the unhealthy subsystem are analyzed at a
second examination level using other selective data from the
plurality of sensors to identify one of the plurality of component
systems as an unhealthy component system.
[0010] In one aspect of the vehicle, a first subsystem-status data
cluster is saved on the memory of the computing device. The first
subsystem-status data cluster defines a range of data values from
the first set of the plurality of sensors indicating that a first
subsystem of the propulsion system is the unhealthy subsystem. The
processor is operable to execute the diagnostic algorithm to
compare data sensed from the first set of the plurality of sensors
to the first subsystem-status data cluster to determine if the data
sensed from the first set of the plurality of sensors is within the
first subsystem-status data cluster, or if the data sensed from the
first set of the plurality of sensors is outside the first
subsystem-status data cluster. The diagnostic algorithm may
indicate that the first subsystem is the unhealthy subsystem when
the data sensed from the first set of the plurality of sensors is
inside of the first subsystem-status data cluster.
[0011] In another aspect of the vehicle, a first component-status
data cluster is saved on the memory of the computing device. The
first component-status data cluster defines a range of data values
from a second set of the plurality of sensors indicating that a
first component system of the first subsystem is the unhealthy
component system. When the first subsystem is the unhealthy
subsystem the processor is operable to execute the diagnostic
algorithm to compare data sensed from the second set of the
plurality of sensors to the first component-status data cluster, to
determine if the data sensed from the second set of the plurality
of sensors is within the first component-status data cluster, or if
the data sensed from the second set of the plurality of sensors is
outside the first component-status data cluster. When the data
sensed from the second set of the plurality of sensors is inside of
the first component-status data cluster, the diagnostic algorithm
may indicate that the first component system of the first subsystem
is the unhealthy component system.
[0012] Accordingly, the diagnostic algorithm may identify the root
cause, i.e., the unhealthy subcomponent of one of the component
systems of one of the subsystems of the propulsion system, causing
the propulsion system to operate outside of the system-healthy
cluster, i.e., range. By using the top-down hierarchical
examination procedure, computational requirements on the computing
device are minimized, because the diagnostic system does not have
to examine each and every component and subcomponent of the
propulsion system. This is because the top-down hierarchical
examination procedure does not examine or analyze the
subcomponents, component systems, and/or the subsystems of the
propulsion system that are healthy.
[0013] The above features and advantages and other features and
advantages of the present teachings are readily apparent from the
following detailed description of the best modes for carrying out
the teachings when taken in connection with the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 is a schematic plan view of a vehicle.
[0015] FIG. 2 is a flow diagram illustrating a top-down
hierarchical examination procedure of a diagnostic system of the
vehicle.
[0016] FIG. 3 is a schematic graph showing a system-healthy and
subsystem-unhealthy data cluster boundaries.
[0017] FIG. 4 is a schematic graph showing component-unhealthy data
cluster boundaries.
[0018] FIG. 5 is a flow chart illustrating a method of diagnosing a
propulsion system of the vehicle.
DETAILED DESCRIPTION
[0019] Those having ordinary skill in the art will recognize that
terms such as "above," "below," "upward," "downward," "top,"
"bottom," etc., are used descriptively for the figures, and do not
represent limitations on the scope of the disclosure, as defined by
the appended claims. Furthermore, the teachings may be described
herein in terms of functional and/or logical block components
and/or various processing steps. It should be realized that such
block components may be comprised of a number of hardware,
software, and/or firmware components configured to perform the
specified functions.
[0020] Referring to the FIGS., wherein like numerals indicate like
parts throughout the several views, a vehicle is generally shown at
20 in FIG. 1. Referring to FIG. 1, the vehicle 20 may include a
type of moveable platform, such as but not limited to a car, a
truck, a van, a tractor, a boat, a plane, an ATV, a UTV, etc. The
vehicle 20 includes a propulsion system 22. The propulsion system
22 may include, but is not limited to, an internal combustion
engine 24, an electric motor 26, an energy storage device 28, e.g.,
a battery, a transmission 30, a transfer case (not shown), one or
more drive axles (not shown), a differential gear set (not shown),
a wheel braking system (not shown), a fuel system 32, an air intake
system 34, an exhaust system 36, an ignition system 37, etc. The
propulsion system 22 may be configured with just the internal
combustion engine 24 to provide propulsive power for the vehicle
20, just the electric motor 26 to provide propulsive power for the
vehicle 20, a combination of the internal combustion engine 24 and
the electric motor 26 to provide propulsive power for the vehicle
20, or some other combination of components and systems not
described herein that provide the propulsive power for the vehicle
20. As shown in the Figures and described herein, the propulsion
system 22 is embodied as a hybrid system having both the internal
combustion engine 24 and the electric motor 26. However, the
teachings of this disclosure are not limited to the exemplary
hybrid system shown and described herein.
[0021] Referring to FIG. 2, the propulsion system 22, however
configured, includes a plurality of subsystems 42, 44, 46, 48. As
shown in FIG. 2, the propulsion system 22 is generally shown at the
top or highest level 39 of a hierarchical structure 38. Below the
propulsion system 22, at a first level 40 of the hierarchical
structure 38, the subsystems 42, 44, 46, 48 of the propulsion
system 22 are generally shown. The specific number and type of
subsystems included in the propulsion system 22 vary with the type
and configuration of the propulsion system 22. Propulsion systems
22 configured differently than the exemplary embodiment described
herein and shown in FIG. 1 will include different subsystems. The
subsystems 42, 44, 46, 48 of the exemplary propulsion system 22 may
include, but are not limited to, the internal combustion engine 24,
the transmission 30, the electric motor 26, and the energy storage
device 28. The subsystems may be defined based on a particular
function provided to the overall operation of the propulsion system
22.
[0022] As noted above, the subsystems of the propulsion system 22
may differ from the exemplary subsystems 42, 44, 46, 48 described
herein, and generally shown on the first level 40 of the
hierarchical structure 38 of the propulsion system 22. As such, the
subsystems of the propulsion system 22 may be described as a first
subsystem 42, a second subsystem 44, a third subsystem 46, a fourth
subsystem 48, etc. The first subsystem 42 may be defined to include
one of the subsystems of the propulsion system 22. The second
subsystem 44 may be defined to include one of the remaining
subsystems of the propulsion system 22, and so on. As such, the
first subsystem 42 is used herein to generically refer to one of
the subsystems of the propulsion system 22. As such, as used herein
with reference to the exemplary embodiment shown in FIG. 1, the
first subsystem 42 may be defined to include one of the internal
combustion engine 24, the transmission 30, the electric motor 26,
or the energy storage device 28. The second subsystem 44 is used
herein to generically refer to one of the remaining subsystems of
the propulsion system 22 not defined as the first subsystem 42.
[0023] Each of the individual subsystems 42, 44, 46, 48 may further
include one or more component systems 54, 56, 58, 60. As shown in
FIG. 2, the different component systems 54, 56, 58, 60 for each
subsystem are generally shown at a second level 50 of the
hierarchical structure 38. For example, the component systems 54,
56, 58, 60 of the internal combustion engine 24 may include the air
intake system 34, the fuel system 32, the exhaust system 36, the
ignition system 37, etc. Each of the component systems 54, 56, 58,
60 of each respective subsystem 42, 44, 46, 48 may further include
one or more subcomponents 62, 64, 66. As shown in FIG. 2, the
different subcomponents 62, 64, 66 for each component system 54,
56, 58, 60 are generally shown at a third level 52 of the
hierarchical structure 38. For example, the fuel system 32 of the
internal combustion engine 24 may include subcomponents including,
but not limited to, a fuel pump (not shown), a fuel filter (not
shown), fuel injectors (not shown), etc. The air intake system 34
of the internal combustion engine 24 may include subcomponents
including, but not limited to, an air filter (not shown), a
throttle (not shown), etc. The propulsion system 22 may be further
decomposed into additional levels of the hierarchical structure 38.
Accordingly, it should be understood that the teachings of the
disclosure are not limited to the exemplary hierarchical structure
38 shown in FIG. 2 and described herein.
[0024] As noted above, the component systems 54, 56, 58, 60 of each
respective subsystem 42, 44, 46, 48 may differ from the exemplary
component systems described herein, and generally shown on the
second level 50 of the hierarchical structure 38 of the propulsion
system 22. As such, the component systems of each respective
subsystem may be described as a first component system 54, a second
component system 56, a third component system 58, a fourth
component system 60, etc. The first component system 54 may be
defined to include one of the component systems of its' respective
subsystem. The second component system 56 may be defined to include
one of the remaining component systems of its respective subsystem,
and so on. The first component system 54 is used herein to
generically refer to one of the component systems of the first
subsystem 42. As such, as used herein with reference to the
exemplary embodiment shown in FIG. 1, if the first subsystem 42 is
defined to include the internal combustion engine 24, then the
first component system 54 may be defined to include one of the
intake air system, the fuel supply system, the exhaust system 36,
or the ignition system 37.
[0025] Similarly, as noted above, the subcomponents of each
respective component system may differ from the exemplary
subcomponents described herein, and generally shown on the third
level 52 of the hierarchical structure 38 of the propulsion system
22. As such, the subcomponents of each respective component system
may be described as a first subcomponent 62, a second subcomponent
64, a third subcomponent 66, etc. The first subcomponent 62 may be
defined to include one of the components of its' respective
component system. The second subcomponent 64 may be defined to
include one of the remaining components of its' respective
component system, and so on. As such, the first subcomponent 62 is
used herein to generically refer to one of the subcomponents of the
first component system 54.
[0026] Referring to FIG. 1, the vehicle 20 further includes a
plurality of sensors 68. The sensors 68 are operable to sense data
related to the operation of the propulsion system 22. The sensors
68 may be configured to sense data needed to assess the operation
of the propulsion system 22. The specific type, configuration, and
number of sensors 68 will vary with different configurations of the
propulsion system 22. As such, the sensors 68 may sense data
related to rotational speed of a feature, e.g., a crankshaft or
transmission shaft, torque, air flow, oxygen levels, electric
voltage levels, electric current levels, acceleration levels, fluid
levels, etc. Each sensor 68 provides a data stream related to a
particular type of data for a feature of the propulsion system 22.
As such, the plurality of sensors 68, as a whole, provide several
different types of data for several different aspects of the
operation of the propulsion system 22. The specific types of data
provided by the sensors 68, and the specific type and operation of
the sensors 68, is not pertinent to the teachings of this
disclosure, are understood by those skilled in the art, and are
therefore not described in detail herein.
[0027] The vehicle 20 further includes a diagnostic system 70. The
diagnostic system 70 is disposed in communication with the sensors
68, and is operable to receive data from the sensors 68. The
diagnostic system 70 includes a computing device 72 having a memory
74 and a processor 76. The memory 74 of the computing device 72
includes a system-healthy data cluster 78, a first subsystem-status
data cluster 80 for the first subsystem 42, a first
component-status data cluster 82, and a diagnostic algorithm 84
stored thereon.
[0028] Referring to FIG. 3, the system-healthy data cluster 78
defines an inclusive range of data values for a first set 102 of
the sensors 68. Data points obtained and/or processed from the data
values from the first set 102 of the sensors 68 that are within the
inclusive range of the system-healthy data cluster 78 indicate that
the propulsion system 22 is healthy, whereas data points obtained
and/or processed from the data values from the first set 102 of the
sensors 68 that are outside the inclusive range of the
system-healthy data cluster 78 indicate that the propulsion system
22 is unhealthy. Because data from one or more of the sensors 68
may be used to analyze a specific subcomponent of one of the
component systems, and may not be required to determine the overall
health of the propulsion system 22, the first set 102 of the
sensors 68 includes a defined subset of the available sensors 68 of
the vehicle 20. As such, the first set 102 of the sensors 68 does
not include each of the available sensors 68.
[0029] The first set 102 of the sensors 68 includes a minimal
number of sensors 68 to provide the minimal data to describe the
healthy/unhealthy state of the propulsion system 22, and if the
propulsion system 22 is unhealthy, identify which subsystem 42, 44,
46, 48 is unhealthy. Some data may be used and/or processed to
define derived variables of sensor measurements that describe the
healthy/unhealthy state of the propulsion system 22, e.g., the
system-healthy data cluster 78. For example, the variables
associated with the healthy/unhealthy state of the internal
combustion engine 24 may include an engine torque/speed output in
response to defined inputs, e.g., throttle angle, fuel pulse-width,
etc. A model of the engine torque generation may be used to compute
an error between the expected torque and measured torque. This
error signal may be used to determine if the internal combustion
engine 24 is developing the right amount of torque, i.e., is
healthy, or not. In other words, the error signal is the data
compared to the system-healthy data cluster 78. Similarly, the air
intake system may be evaluated with respect to a fresh air amount
delivered into the combustion chamber by blending a sensor measure
with a model generating the expected air amount. The top-down
hierarchical structure 38 enables the use of key variables/data
measurements to check the operation of a particular system,
subsystem or component, thereby minimizing the number of sensors 68
required to evaluate each system, subsystem, or component.
[0030] The first subsystem-status data cluster 80 may include a
range that defines a healthy status or an unhealthy status. The
range may be inclusive, or exclusive. As described herein, the
first subsystem-status data cluster 80 is described as the first
subsystem-status unhealthy data cluster 80. The first
subsystem-unhealthy data cluster 80 defines a range of data values
from the first set 102 of the sensors 68. Data points obtained
and/or processed from the data values from the first set 102 of the
sensors 68 that are within the inclusive range of the first
subsystem-unhealthy data cluster 80 indicate that the first
subsystem 42 is unhealthy. Data points obtained and/or processed
from the data values from the first set 102 of the sensors 68 that
are outside the inclusive range of the first subsystem-unhealthy
data cluster 80 are inconclusive as to the health of the first
subsystem 42.
[0031] The first component-status data cluster 82 may include a
range that defines a healthy status or an unhealthy status. The
range may be inclusive, or exclusive. As described herein, the
first component-status data cluster 82 is described as the first
component-status unhealthy data cluster 82. The first
component-unhealthy data cluster 82 defines a range of data values
from a second set 104 of the sensors 68. Data points obtained
and/or processed from the data values from the second set 104 of
the sensors 68 that are within the inclusive range of the first
component-unhealthy data cluster 82 indicate that the first
component system 54 of the first subsystem 42 is unhealthy. Data
points obtained and/or processed from the data values from the
second set 104 of the sensors 68 that are outside the inclusive
range of the first component-unhealthy data cluster 82 are
inconclusive as to the health of the first component system 54.
[0032] Because data from one or more of the sensors 68 may be used
to analyze a specific subcomponent of the first component system
54, or a different component system, e.g., the second component
system 56, the data from one or more of the sensors 68 may not be
required to determine the overall health of the first component
system 54. As such, the second set 104 of the sensors 68 includes a
defined subset of the available sensors 68 of the vehicle 20. As
such, the second set 104 of the sensors 68 does not include each of
the available sensors 68. Additionally, the sensors 68 included in
the second set 104 of the sensors 68 may differ from the sensors 68
included in the first set 102 of the sensors 68. The second set 104
of the sensors 68 includes a minimal number of sensors 68 to
provide the minimal data to describe the healthy/unhealthy state of
the first component system 54. Some data may be used and/or
processed to define derived variables of sensor measurements that
describe the healthy/unhealthy state of the first component system
54, e.g., the first component un-healthy data cluster 82.
[0033] The subsystem-unhealthy data clusters and the
component-unhealthy data clusters described herein may be
considered to define a specific failure mode of hardware for a
given subsystem or component. For example, different failure modes
may result in the fuel system 32 being unhealthy. The different
failure modes for the fuel system 32 may include, but are not
limited to, a fuel injector leak causing over fueling or a fuel
injector clog causing under fueling. Although both of these failure
modes are related to the same hardware, i.e., a fuel injector, each
of these different failure modes may include a respective
component-unhealthy data cluster to define each respective failure
mode. As such, it should be appreciated that each
subsystem-unhealthy data cluster and/or each component-unhealthy
data cluster may define a specific failure mode. Furthermore, it
should be appreciated that the number of subsystem-unhealthy data
clusters and component-unhealthy data clusters may vary from the
exemplary embodiments described herein.
[0034] The computing device 72 may be referred to as a computer, a
control module, a control unit, a vehicle controller, a controller,
etc. The computing device 72 analyzes the data obtained by the
sensors 68 to diagnose the health of the propulsion system 22. As
noted above, the computing device 72 includes the memory 74 and the
processor 76. Additionally, the computing device 72 may include
other software, hardware, memory, algorithms, connections, sensors,
etc., to diagnose the health of the propulsion system 22. As such,
a method, described below and generally shown in FIG. 5, may be
embodied as a program or algorithm at least partially operable on
the computing device 72. It should be appreciated that the
computing device 72 may include a device capable of analyzing data
from the various sensors 68, comparing the data, and making the
decisions required to diagnose the health of the propulsion system
22.
[0035] Additionally, the computing device 72 may include a
communication link to an off-vehicle server or computer, and/or be
configured for processing data in the Cloud as is understood by
those in the art. As such, data from the vehicle may be
communicated to an off-vehicle computer or system, so that at least
some of the processes of the algorithm described herein may be
performed on computers or systems located off-vehicle and/or in the
Cloud. For example, certain data from a set of the sensors, or
variables calculated from sensor data, may be communicated to the
Cloud, whereupon the communicated data may be processed and
analyzed and the results communicated back to the computing device
72 of the vehicle 20, to another data processing center, or to a
service facility. As such, it should be appreciated that some
aspects of the algorithm described herein may be executed onboard
the vehicle 20 by the computing device 72, or may be executed
offboard the vehicle 20 by another computer programmed to execute
the specific processes. As such, while the disclosure generally
describes the computing device 72 of the vehicle executing the
diagnostic algorithm 84 described herein, it should be appreciated
that the scope of the disclosure is not limited to the computing
device 72 of the vehicle 20 executing the entirety of the described
diagnostic algorithm 84, and that the scope of the disclosure
includes using off vehicle systems for executing one or more
aspects of the diagnostic algorithm 84. As such, the computing
device 72 may be interpreted broadly to include other computing
systems located remote from the vehicle 20, but that are connected
to the computing device 72 on the vehicle for communication
therebetween.
[0036] The computing device 72 may be embodied as one or multiple
digital computers or host machines each having one or more
processors, read only memory (ROM), random access memory (RAM),
electrically-programmable read only memory (EPROM), optical drives,
magnetic drives, etc., a high-speed clock, analog-to-digital (A/D)
circuitry, digital-to-analog (D/A) circuitry, input/output (I/O)
circuitry, I/O devices, and communication interfaces, as well as
signal conditioning and buffer electronics.
[0037] The computer-readable memory 74 may include
non-transitory/tangible medium which participates in providing data
or computer-readable instructions. Memory 74 may be non-volatile or
volatile. Non-volatile media may include, for example, optical or
magnetic disks and other persistent memory. Example volatile media
may include dynamic random access memory (DRAM), which may
constitute a main memory. Other examples of embodiments for the
memory 74 include a floppy, flexible disk, or hard disk, magnetic
tape or other magnetic medium, a CD-ROM, DVD, and/or other optical
medium, as well as other possible memory devices such as flash
memory.
[0038] The computing device 72 includes the tangible,
non-transitory memory 74 on which are recorded computer-executable
instructions, including the diagnostic algorithm 70. The processor
76 of the computing device 72 is configured for executing the
diagnostic algorithm 70. The diagnostic algorithm 70 implements a
method of diagnosing the propulsion system 22 of the vehicle
20.
[0039] Referring to FIG. 5, the method includes defining the first
set 102 of the sensors 68 of the vehicle 20 for evaluating an
overall health of the propulsion system 22, and defining the second
set 104 of the sensors 68 for evaluating the health of the
component systems 54, 56, 58, 60. The steps of defining the first
set 102 and the second set 104 of the sensors 68 is generally
indicated by box 120 in FIG. 5. As noted above, the vehicle 20
includes the plurality of sensors 68, with each sensor 68 operable
to sense data related to a certain function or operation. The first
set 102 of the sensors 68 includes the sensors 68 that are needed
to evaluate the overall status of the propulsion system 22. The
specific data needed to evaluate the overall health of the
propulsion system 22 is dependent upon the specific configuration
and features of the propulsion system 22. In addition, the first
set 102 of the sensors 68 may include the sensors 68 that are
needed to evaluate the health of the first subsystem 42 of the
propulsion system 22. The specific data needed to evaluate the
health of the first subsystem 42 is dependent upon the specific
operation and/or function of the first subsystem 42.
[0040] Similarly, the second set 104 of the sensors 68 of the
vehicle 20 for evaluating the component systems 54, 56, 58, 60 of
the first subsystem 42 is also defined. As noted above, the vehicle
20 includes the plurality of sensors 68, with each sensor 68
operable to sense data related to a certain function or operation.
The second set 104 of the sensors 68 includes those sensors 68 that
are needed to evaluate the health of the first component system 54.
The specific data needed to evaluate the health of the propulsion
system 22 is dependent upon the specific operation and/or function
of the first component system 54.
[0041] The system-healthy data cluster 78 for evaluating the
overall health of the propulsion system 22, the first
subsystem-unhealthy data cluster 80 for determining if the first
subsystem 42 is unhealthy, and the first component-unhealthy data
cluster 82 for determining if the first component system 54 of the
first subsystem 42 is unhealthy are also defined, and saved in the
memory 74 of the computing device 72. The step of defining the data
clusters for the diagnostic system 70 is generally indicated by box
122 in FIG. 5. The system-healthy data cluster 78 may be defined by
examining certain data from certain sensors 68 of the vehicle 20
when the propulsion system 22 is appreciated to be operating
properly, i.e., healthy. By looking at the data from the select
sensors 68 of the understood to be healthy propulsion system 22,
the range of values of the data may be defined to establish the
system-healthy data cluster 78 for the propulsion system 22. It
should be appreciated that data from the available sensors 68 is
not required to evaluate the overall operational health of the
propulsion system 22, which is why the first set 102 of the sensors
68 includes a selection of the available sensors 68.
[0042] The first subsystem-unhealthy data cluster 80 may be defined
by examining certain data from certain sensors 68 of the vehicle 20
when the first subsystem 42 is appreciated to be operating
improperly, i.e., unhealthy. By looking at the data from the select
sensors 68 of the appreciated unhealthy first subsystem 42, the
range of values of the data may be defined to establish the first
subsystem-unhealthy data cluster 80. It should be appreciated that
data from the available sensors 68 is not required to evaluate the
overall operational health of the first subsystem 42, which is why
the first set 102 of the sensors 68 includes a selection of the
available sensors 68.
[0043] The first component-unhealthy data cluster 82 may be defined
by examining certain data from certain sensors 68 of the vehicle 20
when the first component system 54 is appreciated to be operating
improperly, i.e., unhealthy. By looking at the data from the select
sensors 68 of the appreciated unhealthy first component system 54,
the range of values of the data may be defined to establish the
first component-unhealthy data cluster 82. It should be appreciated
that data from the available sensors 68 is not required to evaluate
the overall operational health of the first component system 54,
which is why the second set 104 of the sensors 68 includes a
selection of the available sensors 68.
[0044] Data from the first set 102 of the sensors 68 is sensed, and
communicated to the computing device 72. The step of sensing data
with the first set 102 of sensors 68 is generally indicated by box
124 in FIG. 5. As noted above, the specific type of data and the
specific type of sensors 68 used to obtain the data is dependent
upon the specific configuration of the propulsion system 22. The
computing device 72 may, in some circumstances, manipulate the data
sensed from the first set 102 of the sensors 68 to define one or
more data values. The data values may then be compared to the
system-healthy data cluster 78, and/or the first
subsystem-unhealthy data cluster 80 respectively. The data values
may represent computational or functional values that are used to
evaluate the propulsion system 22 and/or the first subsystem 42. As
such, it should be appreciated that the data directly from the
sensors 68, or data values calculated from the data directly
obtained from the sensors 68, may be used by the diagnostic
algorithm 70 to determine the health of the propulsion system
22.
[0045] Once the data from the first set 102 of the sensors 68 has
been obtained, then the diagnostic algorithm 70 may compare the
data sensed from the first set 102 of the sensors 68 to the
system-healthy data cluster 78. The step of comparing the sensed
data from the first set 102 of the sensors 68 to the system-healthy
data cluster 78 is generally indicated by box 126 in FIG. 5 The
diagnostic algorithm 70 makes this comparison to determine if the
data sensed from the first set 102 of the sensors 68 is within the
system-healthy data cluster 78, or if the data sensed from the
first set 102 of the sensors 68 is outside the system-healthy data
cluster 78. If the diagnostic algorithm 70 determines that the data
sensed from the first set 102 of the plurality of sensors 68 is
inside the system-healthy data cluster 78, generally indicated at
128 then the diagnostic algorithm 70 may indicate that the
propulsion system 22 is healthy. If the overall health of the
propulsion system 22 is healthy, then the diagnostic algorithm 70
may end, and perform no additional analysis of the propulsion
system 22. The step of ending the diagnostic algorithm 70 is
generally indicated by box 130 in FIG. 5. By doing so, the
diagnostic algorithm 70 uses a top-down approach for diagnosing the
propulsion system 22. If the overall health of the propulsion
system 22 is determined to be healthy, there is no need to use
additional computing power and resources to examine the remaining
subsystems, component systems, and subcomponents of the propulsion
system 22. This top-down diagnostic approach to the propulsion
system 22 increases computational efficiency relative to a
traditional bottom-up approach, which examines most of the
subcomponents, component systems, and subsystems of the vehicle 20,
regardless of whether or not the propulsion system 22 is operating
properly, i.e., healthy. The diagnostic algorithm 70 described
herein performs additional analyses when the propulsion system 22
is found to be unhealthy, not when it is found to be healthy.
[0046] If the diagnostic algorithm 70 determines that the data
sensed from the first set 102 of the sensors 68 is outside the
system-healthy data cluster 78, generally indicated at 132, then
the diagnostic algorithm 70 may indicate that the propulsion system
22 is unhealthy. The diagnostic algorithm 70 may indicate that the
propulsion system is unhealthy in a suitable manner, such as by
lighting an indicator lamp, displaying a written message on a
display screen, broadcasting an audio message, etc. When the
overall health of the propulsion system 22 is determined to be
unhealthy, then the diagnostic algorithm 70 further analyzes the
propulsion system 22 using the top-down hierarchical examination
procedure, in which the subsystems of the propulsion system 22 are
analyzed at the first level 40 using selective data from the
sensors 68 to identify one of the subsystems as an unhealthy
subsystem, and then the component systems of the unhealthy
subsystem are analyzed at the second level 50 using other selective
data from the sensors 68 to identify one of the component systems
as an unhealthy component system.
[0047] Additional examination levels may also be executed if
needed. For example, subcomponents of the unhealthy component
system may be analyzed at a third examination level using other
selective data from the sensors 68 to identify one of the
subcomponents of the unhealthy component system as an unhealthy
subcomponent. It should be appreciated that the number of
examination levels is dependent upon the specific configuration of
the propulsion system 22. As such, the top-down hierarchical
examination procedure described herein is not limited to the
exemplary number of examination levels, and that the number of
examination levels may be greater or fewer than the number of
examination levels described herein.
[0048] Each examination level of the top-down hierarchical
examination procedure includes a defined number of data inputs,
i.e., a specific number of the sensors 68 providing data for each
examination level, and a defined number of possible outputs. The
possible outputs may be limited to either healthy or unhealthy for
a specific subsystem or component system. However, in other
embodiments, each level may include multiple data clusters, with
each different data cluster used to identify a specific unhealthy
feature of a subsystem or component system. For example, referring
to FIG. 3, the first subsystem-unhealthy data cluster 80 is shown,
along with a second subsystem-unhealthy data cluster 90, a third
subsystem-unhealthy data cluster 92, and a fourth
subsystem-unhealthy data cluster 94. The data sensed from the first
set 102 of the sensors 68 is generally shown by the point 106. If
the sensed data from the first set 102 of sensors 68 falls within
the first subsystem-unhealthy data cluster 80, then the diagnostic
algorithm 70 may determine that the first subsystem 42 is
unhealthy, and conduct further analysis on the component systems of
the first subsystem 42. However, if the sensed data from the first
set 102 of sensors 68 falls within the second subsystem-unhealthy
data cluster 90, then the diagnostic algorithm 70 may determine
that the second subsystem 44 is unhealthy, and conduct further
analysis on the component systems of the second subsystem 44. By so
doing, the computational resources of the computing device 72 are
directed to identifying the features of the propulsion system 22
that are unhealthy, instead of confirming the proper functionality
of the other features of the propulsion system 22 that are
healthy.
[0049] Referring to FIG. 3, when the overall health of the
propulsion system 22 is determined to be unhealthy, then the
diagnostic algorithm 70 compares the data sensed from the first set
102 of the sensors 68 to the first subsystem-unhealthy data cluster
80. The step of comparing the data from the first set 102 of the
sensors 68 to the subsystem-unhealthy data clusters 80, 90, 92, 94
is generally indicated by box 134 in FIG. 5. The diagnostic
algorithm 70 makes this comparison to determine if the data sensed
from the first set 102 of the plurality of sensors 68 is within one
of the subsystem-unhealthy data clusters 80, 90, 92, 94, or if the
data sensed from the first set 102 of the plurality of sensors 68
is outside the subsystem-unhealthy data clusters 80, 90, 92,
94.
[0050] When the data sensed from the first set 102 of the sensors
68 is not inside the subsystem-unhealthy data clusters 80, 90, 92,
94, generally indicated at 136, then the diagnostic algorithm 70
may indicate that the propulsion system 22 is unhealthy, but the
cause is not identifiable. The step of indicating that the cause of
the unhealthy propulsion system 22 is not identifiable is generally
indicated by box 138 in FIG. 5.
[0051] When the data sensed from the first set 102 of the sensors
68 is inside one of the subsystem-unhealthy data clusters 80, 90,
92, 94, generally indicated at 140, the diagnostic algorithm 70 may
identify which one of the subsystems 42, 44, 46, 48 is the
unhealthy subsystem. The step of identifying the unhealthy
subsystem 42, 44, 46, 48 is generally indicated by box 142 in FIG.
5. The diagnostic algorithm 70 may indicate that the unhealthy
subsystem in a suitable manner, such as by lighting an indicator
lamp, displaying a written message on a display screen,
broadcasting an audio message, etc. For example, if the data sensed
from the first set 102 of the sensors 68 is inside the first
subsystem-unhealthy data cluster 80, then the diagnostic algorithm
70 may indicate the first subsystem 42 is the unhealthy subsystem.
It should be appreciated that the described analysis of the first
subsystem 42 is exemplary, and that the diagnostic algorithm 70 may
execute similar comparisons for the other subsystems of the
propulsion system 22, e.g., compare the sensed data from the first
set 102 of sensors 68 to the second subsystem-unhealthy data
cluster 90 to determine if the second subsystem 44 is unhealthy, or
compare the sensed data from the first set 102 of sensors 68 to the
third subsystem-unhealthy data cluster 92 to determine if the third
subsystem 46 is unhealthy, etc. By so doing, the diagnostic
algorithm 70 may identify which one of the subsystems is unhealthy,
and may be causing the propulsion system 22 to be unhealthy.
[0052] Once the diagnostic algorithm 70 determines which one of the
subsystems of the propulsion system 22 is unhealthy, e.g., that the
first subsystem 42 is unhealthy, then the diagnostic algorithm 70
analyzes the component systems of the unhealthy subsystem, e.g.,
the first component system 54, of the unhealthy first subsystem 42.
The diagnostic algorithm 70 senses data from the second set 104 of
the sensors 68. The step of sensing data from the second set 104 of
the sensors 68 is generally indicated by box 143 in FIG. 5.
[0053] Referring to FIG. 4, the first component-unhealthy data
cluster 82 is shown, along with a second component-unhealthy data
cluster 96, a third component-unhealthy data cluster 98, and a
fourth component-unhealthy data cluster 100. The data sensed from
the second set 104 of the sensors 68 is generally shown by the
point 108. If the sensed data from the second set 104 of sensors 68
falls within the first component-unhealthy data cluster 82, then
the diagnostic algorithm 70 may determine that the first component
system 54 is unhealthy, and conduct further analysis on the
subcomponents of the first component system 54. However, if the
sensed data from the second set 104 of sensors 68 falls within the
second component-unhealthy data cluster 96, then the diagnostic
algorithm 70 may determine that the second component system 56 is
unhealthy, and conduct further analysis on the subcomponents of the
second component system 56. By so doing, the computational
resources of the computing device 72 are directed to identifying
the features of the propulsion system 22 that are unhealthy,
instead of confirming the proper functionality of the other
features of the propulsion system 22 that are healthy.
[0054] Referring to FIG. 4, the diagnostic algorithm 70 compares
the data sensed from the second set 104 of the sensors 68 to the
component-unhealthy data clusters 82, 96, 98, 100, generally
indicated by box 144 in FIG. 5. The diagnostic algorithm 70 makes
this comparison to determine if the data sensed from the second set
104 of the sensors 68 is within one of the component-unhealthy data
clusters 82 96, 98, 100, or if the data sensed from the second set
104 of the sensors 68 is outside the component-unhealthy data
clusters 82, 96, 98, 100. If the diagnostic algorithm 70 determines
that the data sensed from the second set 104 of the sensors 68 is
inside of the first component-unhealthy data cluster 82, then the
diagnostic algorithm 70 may indicate that the first component
system 54 of the first subsystem 42 is the unhealthy component
system. It should be appreciated that the described analysis of the
first component system 54 is exemplary, and that the diagnostic
algorithm 70 may execute similar comparisons for the other
component systems of the first subsystem 42, e.g., compare the
sensed data from the first set 102 of sensors 68 to the second
component-unhealthy data cluster 96 to determine if the second
component system 56 is unhealthy, or compare the sensed data from
the first set 102 of sensors 68 to the third component-unhealthy
data cluster 98 to determine if the third component system 58 is
unhealthy, etc. By so doing, the diagnostic algorithm 70 may
identify which one of the component systems of the first subsystem
42 is unhealthy, and may be causing the first subsystem 42 to be
unhealthy.
[0055] If the diagnostic algorithm 70 determines that data sensed
from the second set 104 of the sensors 68 is not within the
component-unhealthy data clusters 82, 96, 98, 100, generally
indicated at 146, then the diagnostic algorithm 70 may indicate
that the cause of the unhealthy subsystem is not identifiable,
generally indicated by box 148 in FIG. 5. If the diagnostic
algorithm 70 determines that data sensed from the second set 104 of
the sensors 68 is within one of the component-unhealthy data
clusters 82, 96, 98, 100, generally indicated at 150, then the
diagnostic algorithm 70 may identify the unhealthy component system
of the unhealthy subsystem, generally indicated by box 152 in FIG.
5.
[0056] The diagnostic algorithm 70 may continue with the top-down
hierarchical examination process in a like manner until the
underlying cause of the unhealthy propulsion system 22 is
identified. For example, the diagnostic algorithm 70 may determine
that the overall health of the propulsion system 22 is unhealthy,
determine that the internal combustion engine 24 is unhealthy at
the first examination level, 40 determine that the intake air
system 34 is unhealthy at the second examination level 50, and
determine that the throttle actuator is unhealthy at the third
level 52. The diagnostic algorithm 70 may then issue a message
stating, for example, that "The vehicle 20 has a rough idle due to
an engine misfire caused by an issue in the air delivery system
associated with the throttle." The diagnostic algorithm 70 may
issue the message in a suitable manner, such as through a verbal
announcement, a written message, and/or coded into memory 74 of the
computing device 72 as an error code.
[0057] The process described herein improves the operating
efficiency of the computing device 72 by using the top-down
hierarchical examination process to focus the computational
resources of the computing device 72 on locating the underlying
fault in the propulsion system 22, instead of running bottom-up
diagnostic tests that test functionally of the features of the
propulsion system 22, even when they are operating properly. The
top-down hierarchical examination process does not perform
additional diagnostic tests on the subsystems and on the component
systems of each of the subsystems, when the data sensed from the
first set 102 of the sensors 68 indicates that the propulsion
system 22 is healthy.
[0058] The diagnostic algorithm 84 described above may be realized
and/or implemented using machine learning and/or artificial
intelligence, such as but not limited to a neural network (e.g.,
deep convolutional recurrent neural network), a decision tree
(e.g., random forest), etc. For example, a neural network may be
trained with many labeled healthy data clusters, (e.g., data from
various operations when the internal combustion engine is operating
in a healthy state), and unhealthy data clusters (e.g., data
representing faulty air flow when the internal combustion engine is
operating in an unhealthy state that is induced by a possible
air-related failure mode). In general, the input into the neural
network may include the data from each selective set of sensors 68,
and the output of the neural network may include the
healthy/unhealthy state of the system, subsystem, or component,
based on the training of the neural network. It should be
appreciated that the use of a neural network to implement the logic
of the above described diagnostic algorithm 84 is merely one
exemplary way of implementing the logic of the diagnostic algorithm
84, and that the logic of the diagnostic algorithm 84 disclosed
herein may be implemented on the computing device 72 in other
ways.
[0059] The detailed description and the drawings or figures are
supportive and descriptive of the disclosure, but the scope of the
disclosure is defined solely by the claims. While some of the best
modes and other embodiments for carrying out the claimed teachings
have been described in detail, various alternative designs and
embodiments exist for practicing the disclosure defined in the
appended claims.
* * * * *